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54 results for “wifi”
Destination Choice Model including panel datausing WiFi localization in a pedestrian facility
<p>A minimal example of a destination choice model including panel data on EPFL campus. It is based on the output of Danalet<em> et al. </em>(2014)<em>.</em> </p> <p>It runs on Pythonbiogeme. Some variables are removed from the dataset due to privacy issues. Thus, some parameters may not be significant.</p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (Transp. Res. Part C, 2014)
<p>This dataset contains and describes the data used in</p> <p>Danalet, A., Farooq, B., & Bierlaire, M. (2014). A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. <em>Transportation Research Part C: Emerging Technologies</em>, <strong>44</strong>, 146-170. doi:10.1016/j.trc.2014.03.015</p> <p>Specifically it contains WiFi traces, pedestrian Semantically-Enriched Routing Graph (SERG), and Potential Attractivity measure (PAM).</p>
HALOC Dataset | WiFi CSI-based Long-Range Person Localization Using Directional Antennas
<p><strong>WiFi CSI-based Long-Range Person Localization Using Directional Antennas</strong></p> <p>This repository contains the HAllway LOCalization (HALOC) dataset and WiFi system CAD files as proposed in <a href="https://openreview.net/forum?id=AOJFcEh5Eb" target="_blank" rel="noopener">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the HALOC dataset is provided at: <a href="https://github.com/StrohmayerJ/HALOC" target="_blank" rel="noopener">https://github.com/StrohmayerJ/HALOC</a></p> <p><strong>Dataset Description</strong></p> <p>The HALOC dataset comprises six sequences (in .csv format) of synchronized WiFi Channel State Information (CSI) and 3D position labels. Each row in a given .csv file represents a single WiFi packet captured via ESP-IDF, with CSI and 3D coordinates stored in the "data" and ("x", "y", "z") fields, respectively.</p> <p>The sequences are divided into training, validation, and test subsets as follows:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Sequences</strong></td> </tr> <tr> <td>Training</td> <td>0.csv, 1.csv, 2.csv and 3.csv</td> </tr> <tr> <td>Validation</td> <td>4.csv</td> </tr> <tr> <td>Test</td> <td>5.csv</td> </tr> </tbody> </table> <p> </p> <p><strong>WiFi System CAD files</strong></p> <p>We provide CAD files for the 3D printable parts of the proposed WiFi system consisting of the main housing (housing.stl), the lid (lid.stl), and the carrier board (carrier.stl) featuring mounting points for the Nvidia Jetson Orin Nano and the ESP32-S3-DevKitC-1 module. </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] Strohmayer, J., and Kampel, M. (2024). “WiFi CSI-based Long-Range Person Localization Using Directional Antennas”, <em>The Second Tiny Papers Track at ICLR 2024</em>, May 2024, Vienna, Austria. <a href="https://openreview.net/forum?id=AOJFcEh5Eb" target="_blank" rel="noopener">https://openreview.net/forum?id=AOJFcEh5Eb</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{<br>strohmayer2024wifi,<br>title={WiFi {CSI}-based Long-Range Person Localization Using Directional Antennas},<br>author={Julian Strohmayer and Martin Kampel},<br>booktitle={The Second Tiny Papers Track at ICLR 2024},<br>year={2024},<br>url={https://openreview.net/forum?id=AOJFcEh5Eb}<br>}</pre>
3DO Dataset | On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios
<p><strong>On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios</strong></p> <p>This repository contains the <strong>3DO dataset</strong> proposed in <a href="https://doi.org/10.1007/978-3-031-78354-8_13">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the 3DO dataset is provided at: <a href="https://github.com/StrohmayerJ/3DO/tree/main">https://github.com/StrohmayerJ/3DO</a></p> <p><strong>Dataset Description</strong></p> <p>The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (<strong>Note:</strong> The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)</p> <p>The directories <code>3DO/d1/</code>, <code>3DO/d2/</code>, and <code>3DO/d3/</code> contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., <code>3DO/d1/w1/</code>) contains a <code>csiposreg.csv</code> file storing the raw WiFi packet time series and a <code>csiposreg_complex.npy</code> cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, <code>csiposreg_complex.npy</code> is automatically generated by the provided dataloader.)</p> <p>Dataset Structure:</p> <p>/3DO</p> <p>├── d1 <em><-- day 1 subdirectory</em></p> <p> └── w1 <em><-- sequence subdirectory</em></p> <p> └── csiposreg.csv <em><-- raw WiFi packet time series</em></p> <p> └── csiposreg_complex.npy <em><-- CSI time series cache</em></p> <p>├── d2 <-- day 2 subdirectory</p> <p>├── d3 <-- day 3 subdirectory</p> <p> </p> <p>In [1], we use the following training, validation, and test split:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Day</strong></td> <td><strong>Sequences </strong></td> </tr> <tr> <td>Train</td> <td>1</td> <td>w1, w2, w3, s1, s2, s3, l1, l2, l3</td> </tr> <tr> <td>Val</td> <td>1</td> <td>w4, s4, l4</td> </tr> <tr> <td>Test</td> <td>1</td> <td>w5 , s5, l5</td> </tr> <tr> <td>Test</td> <td>2</td> <td>w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5</td> </tr> <tr> <td>Test</td> <td>3</td> <td>w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4</td> </tr> </tbody> </table> <p><em>w = walking, s = sitting and l= lying</em></p> <p><strong>Note: </strong>On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a>.</p> <p><a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a> Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-78354-8_13</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin",<br> title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios",<br> booktitle="Pattern Recognition",<br> year="2025",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="194--211",<br> isbn="978-3-031-78354-8" }</pre>
Wallhack1.8k Dataset | Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition
<p>This repository contains the <strong>Wallhack1.8k dataset</strong> for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the <strong>CAD models</strong> (of 3D-printable parts) of the WiFi systems proposed in [2].</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: <a href="https://github.com/StrohmayerJ/wallhack1.8k" target="_blank" rel="noopener">https://github.com/StrohmayerJ/wallhack1.8k</a></p> <p><strong>Dataset Description</strong></p> <p>The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).</p> <p>To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:</p> <ul> <li>LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system</li> <li>LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system</li> <li>NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system</li> <li>NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system</li> </ul> <p>These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector <em>H</em> being stored in the "data" field and the class label being stored in the "class" field. <em>H </em>is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of <em>H</em> (e.g., via <em>numpy.abs(H)</em>) yields the subcarrier amplitudes <em>A</em>.</p> <p>To extract the 52 L-LTF subcarriers used in [1], the following indices of <em>A</em> are to be selected:</p> <pre><code># 52 L-LTF subcarriers csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]</code></pre> <p>Additional 56 HT-LTF subcarriers can be selected via:</p> <pre><code># 56 HT-LTF subcarriers csi_valid_subcarrier_index += [i for i in range(66, 94)] csi_valid_subcarrier_index += [i for i in range(95, 123)]</code></pre> <p>For more details on subcarrier selection, see <a href="https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html">ESP-IDF</a> (Section Wi-Fi Channel State Information) and <a href="https://github.com/espressif/esp-csi">esp-csi</a>.</p> <p>Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the <em>spectrograms/</em> directory.</p> <p>The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label]</p> <ul> <li>Spectrogram index: [0, ..., n]</li> <li>Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."</li> <li>Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.</li> </ul> <p><strong>Dataset Overview:</strong></p> <p>Table 1: Raw WiFi packet sequences.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td><em>"no presence" / label 0</em></td> <td><em>"walking" / label 1</em></td> <td><em>"walking + arm-waving" / label 2</em></td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4</td> <td>20</td> <td>20</td> <td><strong>44</strong></td> </tr> </tbody> </table> <p>Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td> <p><em>"no presence" / </em> label 0</p> </td> <td> <p><em>"walking"</em> / label 1</p> </td> <td><em>"walking + arm-waving" / </em>label 2</td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>149</td> <td>154</td> <td>155</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>149</td> <td>160</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>148</td> <td>150</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>143</td> <td>147</td> <td>147</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>589</td> <td>611</td> <td>606</td> <td><strong>1,806</strong></td> </tr> </tbody> </table> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2].</p> <p>[1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, <em>In IFIP International Conference on Artificial Intelligence Applications and Innovations</em> (pp. 42-56). Cham: Springer Nature Switzerland<em>,</em> doi: <a href="https://doi.org/10.1007/978-3-031-63211-2_4" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-63211-2_4</a>.</p> <p>[2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: <a href="https://doi.org/10.1109/ICIP51287.2024.10647666" target="_blank" rel="noopener">https://doi.org/10.1109/ICIP51287.2024.10647666</a>.</p> <p>BibTeX citations:</p> <pre>@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}}<br><br>@INPROCEEDINGS{10647666,<br> author={Strohmayer, Julian and Kampel, Martin},<br> booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, <br> title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition}, <br> year={2024},<br> volume={},<br> number={},<br> pages={3594-3599},<br> keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32},<br> doi={10.1109/ICIP51287.2024.10647666}}<br><br><br></pre>
Dataset: WiFi-based Human Activity Recognition using Raspberry Pi
<p>This dataset contains 980 802.11 Channel State Information captures for 11 activities performed in a small apartment by 1 subject. For full description, check README.md.</p>
Free WiFi to monitor flow in Hanoian traditional markets
<p>Despite being the main source of fresh, convenient, and affordable food for 80% of Hanoi’s population, food flows within traditional markets remain largely invisible due to a lack of tracing systems and environmental conditions which make traditional tracking approaches challenging.</p> <p>By providing free internet to a series of wholesalers and markets in the Cau Giay and Dong Anh districts of Hanoi, Vietnam, this project will put in place the first pieces of tracking system that will characterize and monitor food flows between traders, retailers, and consumers.</p> <p>Research has found that 10-40% of traditional market food is contaminated with microbes or parasites which cause foodborne illnesses. As shoppers become increasingly concerned about food safety and large-scale retailers that can offer food safety certification expand rapidly, this project aims to equip traditional market actors with data that could prevent their marginalization through urban policy decisions that may favor organized retailers, as well as improve the safety of traditional market goods.</p> <p>The collected food flow data will allow for improved linkages among key traditional market actors and help identify better policy and planning options for improving distribution channels in ways that benefits under-resourced communities.</p> <p>To implement the project, the Alliance of Bioversity International and CIAT and the General Statistics Office (GSO) of Vietnam survey actors and track space and time data points on all devices within the range of the WiFi routers and signal amplifiers, whether connected to the internet or not.</p> <p>The pilot system ran on three layers of data:</p> <p><strong>Layer One</strong></p> <p>Every smartphone has a unique media access control (MAC) address that the WiFi routers installed in the markers use to identify how many MAC addresses visit the markets over time, how many return to the market and how often, and how markets differ on these metrics. This data is collected even if the smartphone is not connected to the WiFi network.</p> <p><strong>Layer Two</strong></p> <p>When a smartphone user connects to the free WiFi, they are prompted to answer a series of questions depending on their user type (vendor, customer, etc.). For example, a user that identifies as a vendor is asked questions regarding sales of specific commodities which will allow for sales to be characterized across time and space.</p> <p><strong>Layer Three</strong></p> <p>To validate findings in Layer One and Two, in-person surveys were conducted with vegetable, pork and rice sellers in five traditional markets in Hanoi</p> <ul> <li><strong>mac</strong>: An anonymized version of the MAC. All the MAC address were anonymized through a SHA-3 256 hashing function. The hashed mac ensure anonymity while is consistent across all markets and during the whole period of the analysis. We can therefore ensure that a given mac found in two different dataset will correspond to the same phone.</li> <li><strong>market</strong>: The name of the market where the phone was seen</li> <li><strong>role</strong>: Self-identified role if the user connected to the wifi and filled-out the layer 2 form</li> <li><strong>gender</strong>: Self-identified role if the user connected to the wifi and filled-out the layer 2 form</li> <li><strong>median_first_seen: </strong>The median time when the user is first seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user entered the market)</li> <li><strong>median_last_seen: </strong>The median time when the user is last seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user left the market)</li> <li><strong>average_time_day: </strong>The average number of time the user visited the market. A period of time of at least 2 hours between two consecutive observation of the user in the market is needed to be counted as a different visit.</li> <li><strong>average_duration_day: </strong>The average duration spent on the market daily.</li> <li><strong>average_day_week: </strong>The average number of visits per week.</li> <li><strong>average_total_day_seen: </strong>The total number of days a user was seen on the market.</li> <li><strong>total_durantion: </strong>Total duration spent by a single user on the market.</li> </ul>
WLAN (WiFi) RSS database for fingerprinting positioning
<p>This data set contains two WLAN Received Signal Strengths (RSS) databases suitable for fingerprinting positioning. One database contains training data (Training_rss.csv, Training_coordinates.csv), the radio map, the second database contains test data (Test_rss.csv, Test_coordinates.csv), RSS measurements on a path and the coordinates of that path. The data was collected in a three-floor building at Tampere University of Technology.</p> <p>The files Training_rss.csv and Test_rss.csv represent a matrix, with a reference point per row and an access point per column. The radio map consists of 446 reference points and 489 access points. Empty RSS values are set to 100. The files Training_coordinates.csv and Test_coordinates.csv contain the reference positions, 3D coordinates in a metric local reference frame. The data format allows to use previously published software (https://doi.org/10.5281/zenodo.889797) to analyze the data.<br> <br> The data is postprocessed: The reference positions of each floor are mapped onto a regular grid with 5 meter grid point spacing and the RSS values at each reference position are spatial averages of the RSS values in the resulting cells. The test data is mapped as well, but to a grid of 1 meter grid point spacing, from which only every third value was selected.</p> <p> </p>
WiFiCam Dataset | Through-Wall Imaging based on WiFi Channel State Information
<p><strong>Through-Wall Imaging based on WiFi Channel State Information</strong></p> <p>This repository contains the <strong>WiFiCam dataset</strong> for through-wall imaging based on WiFi channel state information proposed in [1]. The corresponding source code repository is located at: <a href="https://github.com/StrohmayerJ/wificam">https://github.com/StrohmayerJ/wificam</a></p> <p>The demo video (demo.mp4) showcases the through-wall imaging capabilities of our approach. </p> <p> </p> <p><strong>Dataset Structure</strong></p> <p>/wificam</p> <p>├── j3</p> <p> └── 320 <-- 320x240 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 92108.png <-- 320x240 RGB image</p> <p> └── 92112.png</p> <p> └── ...</p> <p> └── 640 <-- 640x480 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 154.png <-- 640x480 RGB image</p> <p> └── 155.png</p> <p> └── ...</p> <p>├── statistics320.csv <-- per-channel means and standard deviations for 320x240 images</p> <p>├── statistics640.csv <-- per-channel means and standard deviations for 640x480 images</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] J. Strohmayer, R. Sterzinger, C. Stippel and M. Kampel, "Through-Wall Imaging Based On WiFi Channel State Information," <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 4000-4006, doi: 10.1109/ICIP51287.2024.10647775.</p> <p>BibTeX:</p> <pre>@INPROCEEDINGS{10647775, author={Strohmayer, Julian and Sterzinger, Rafael and Stippel, Christian and Kampel, Martin}, booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, title={Through-Wall Imaging Based On WiFi Channel State Information}, year={2024}, volume={}, number={}, pages={4000-4006}, doi={10.1109/ICIP51287.2024.10647775}}</pre>
WiFi 2.4 GHz Jamming attack scenario P2 measurements using ADALM Pluto and Maia SDR
<p>The dataset comprises physical-layer data measurements (I-Q samples) collected using an ADALM Pluto SDR version B. The original firmware from Analog Devices was replaced with the Maia-SDR Firmware (<a href="https://maia-sdr.org/">https://maia-sdr.org/</a>). The data was gathered within a 250 square meter area of the WIRID-LAB (<a href="https://wirid-lab.umng.edu.co/">https://wirid-lab.umng.edu.co/</a> laboratory at the Military University Nueva Granada.</p> <p>The dataset is divided into two groups of measurements labeled 'JAMMER' and 'NORMAL', each containing 165 files. These files represent data collected from 15 different points across 11 WiFi channels.</p> <ul> <li><strong>NORMAL Group:</strong> Measurements were taken under standard WiFi traffic conditions without any interference from a jammer.</li> <li><strong>JAMMER Group:</strong> Measurements were taken while deploying a Legacy Short Training Field Jammer attack from a static point.</li> </ul> <p>Each .zip compressed file contains data for 15 measurement points, with each point captured over one second at a sampling rate of 15 Msps. The data is formatted according to the Signal Metadata Format (SigMF), with each measurement point having one <code>.sigmf-data</code> file and one <code>.sigmf-meta</code> file.</p> <p>File names indicate the WiFi channel (enumerated from 1 to 11), signal type (Jammer or Normal), and the attacker node's position 'P2'.</p> <p>An accompanying image (Deployment of a Jammer Attack Scenario inside WiridLAB.png) illustrates the test scenario."</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 3. The unified hardware unit based on NodeMcu Lua ESP8266 WiFi development board, ACS712T ELC-30A current sensor, and relay SRD-05VDC-SL-C
<p>The software consists of two parts, low-level Arduino sketches and high-level C# Windows form appplication. They are connected using the open-source message MQTT broker Mosquitto.11 Every hardware unit has the unique identifier and commands to control the relay. The MQTT topic “/VPP/Relays” is used by subscribers and publishers. The number “50” sent from C# Windows form (it equals number “2” sent from the standard Mosquitto publisher) is a command to switch on the second relay, “51” (“3”) – to switch off, respectively. The prototype was developed with one root controller and two descendant relays. The commands are as follows: “52” (“4”) / “53” (“5”) – to switch on / off the first relay, “54” (“6”) / “55” (“7”) – to switch on / off the third relay, respectively. This solution is similar to the one presented in [22], but ACS712T ELC-30A current sensor and ESP8266WiFi.h library are applied here. In addition, other commands, e.g. “56” (“8”) to get the value of the current in the 3rd segment, are in use as well.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 5. An example of smart lighting using NodeMcu Lua ESP8266 ESP-12 WiFi board
<p> for different purposes together with switching on/off relays, e.g. to control the motors, to acquire the data from sensors. It allows developing multifunctional smart systems. For instance, the smart lighting unit is created using NodeMcu Lua ESP8266 ESP-12 WiFi board, Arduino light sensor, and relay SRD-05VDC-SL-C, which controls the power supply of the lamp. Figure 5 shows a simplified example of smart lighting, where the lamp is represented by eight 5 mm light-emitting diodes (LEDs).</p>
BASMATI WiFi Localization Dataset
<p><strong>Description</strong></p> <p>This is a dataset that was created in the frame of the research project "BASMATI" an EU-funded research project (grant agreement no. 723131, website: http://www.basmati.cloud). It presents the Received Signal Strength Indicator (RSSI) of the smartphone WiFi transceivers of the attendants of a music festival in a specific day, as it was recorded by a number of WiFi access points.</p> <p>The dataset is comprised of two files:</p> <p>- BASMATI_lastday_RSSI.csv that lists the timestamped RSSI of mobile phones as it was received from the access points.</p> <p>- BASMATI_AccessPoints_locations.csv that lists the fixed locations of the access points</p> <p><strong>Format</strong></p> <p>Each row in BASMATI_lastday_RSSI.csv is comprised of 4 fields:</p> <p>- The id of the access point that intercepted the WiFi signal (values: 1-27 with the exception of 7, 13 and 20)</p> <p>- The timestamp in Unix time</p> <p>- a pseudo, unique user id (values: 1-330846)</p> <p>- RSSI, expressed in dB (values: -103 - -11)</p> <p>Each row in BASMATI_AccessPoints_locations.csv is comprised of 3 fields:</p> <p>- The id of the access point (values: 1-27)</p> <p>- The latitude expressed in degrees, shifted by a fixed number so as to normalise the values in a new reference system and mask the actual positions of the attendants</p> <p>- The longitude expressed in degrees, shifted by a fixed number so as to normalise the values in a new reference system and mask the actual positions of the attendants</p>
EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching
<p><strong>EyeFi Dataset</strong></p> <p>This dataset is collected as a part of the EyeFi project at Bosch Research and Technology Center, Pittsburgh, PA, USA. The dataset contains WiFi CSI values of human motion trajectories along with ground truth location information captured through a camera. This dataset is used in the following paper "<em>EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching</em>" that is published in the IEEE International Conference on Distributed Computing in Sensor Systems 2020 (DCOSS '20). We also published a dataset paper titled as "<em>Dataset: Person Tracking and Identification using Cameras and Wi-Fi Channel State Information (CSI) from Smartphones</em>" in Data: Acquisition to Analysis 2020 (DATA '20) workshop describing details of data collection. Please check it out for more information on the dataset.</p> <p><strong>Data Collection Setup</strong><br> <br> In our experiments, we used Intel 5300 WiFi Network Interface Card (NIC) installed in an Intel NUC and Linux CSI tools [1] to extract the WiFi CSI packets. The (x,y) coordinates of the subjects are collected from Bosch Flexidome IP Panoramic 7000 panoramic camera mounted on the ceiling and Angle of Arrivals (AoAs) are derived from the (x,y) coordinates. Both the WiFi card and camera are located at the same origin coordinates but at different height, the camera is location around 2.85m from the ground and WiFi antennas are around 1.12m above the ground.</p> <p>The data collection environment consists of two areas, first one is a rectangular space measured 11.8m x 8.74m, and the second space is an irregularly shaped kitchen area with maximum distances of 19.74m and 14.24m between two walls. The kitchen also has numerous obstacles and different materials that pose different RF reflection characteristics including strong reflectors such as metal refrigerators and dishwashers. </p> <p>To collect the WiFi data, we used a Google Pixel 2 XL smartphone as an access point and connect the Intel 5300 NIC to it for WiFi communication. The transmission rate is about 20-25 packets per second. The same WiFi card and phone are used in both lab and kitchen area.</p> <p><strong>List of Files</strong><br> Here is a list of files included in the dataset:</p> <pre><code>|- 1_person |- 1_person_1.h5 |- 1_person_2.h5 |- 2_people |- 2_people_1.h5 |- 2_people_2.h5 |- 2_people_3.h5 |- 3_people |- 3_people_1.h5 |- 3_people_2.h5 |- 3_people_3.h5 |- 5_people |- 5_people_1.h5 |- 5_people_2.h5 |- 5_people_3.h5 |- 5_people_4.h5 |- 10_people |- 10_people_1.h5 |- 10_people_2.h5 |- 10_people_3.h5 |- Kitchen |- 1_person |- kitchen_1_person_1.h5 |- kitchen_1_person_2.h5 |- kitchen_1_person_3.h5 |- 3_people |- kitchen_3_people_1.h5 |- training |- shuffuled_train.h5 |- shuffuled_valid.h5 |- shuffuled_test.h5 View-Dataset-Example.ipynb README.md </code></pre> <p>In this dataset, folder `1_person/` , `2_people/` , `3_people/` , `5_people/`, and `10_people/` contains data collected from the lab area whereas `Kitchen/` folder contains data collected from the kitchen area. To see how the each file is structured, please see below in section <em>Access the data</em><strong>.</strong> </p> <p>The training folder contains the training dataset we used to train the neural network discussed in our paper. They are generated by shuffling all the data from `1_person/` folder collected in the lab area (`1_person_1.h5` and `1_person_2.h5`). </p> <p><strong>Why multiple files in one folder?</strong></p> <p>Each folder contains multiple files. For example, `1_person` folder has two files: `1_person_1.h5` and `1_person_2.h5`. Files in the same folder always have the same number of human subjects present simultaneously in the scene. However, the person who is holding the phone can be different. Also, the data could be collected through different days and/or the data collection system needs to be rebooted due to stability issue. As result, we provided different files (like `1_person_1.h5`, `1_person_2.h5`) to distinguish different person who is holding the phone and possible system reboot that introduces different phase offsets (see below) in the system. </p> <p><strong>Special note:</strong></p> <p>For `1_person_1.h5`, this file is generated by the same person who is holding the phone, and `1_person_2.h5` contains different people holding the phone but only one person is present in the area at a time. Boths files are collected in different days as well.</p> <p><br> <strong>Access the data</strong><br> To access the data, hdf5 library is needed to open the dataset. There are free HDF5 viewer available on the official website: <a href="https://www.hdfgroup.org/downloads/hdfview/">https://www.hdfgroup.org/downloads/hdfview/</a>. We also provide an example Python code <em>View-Dataset-Example.ipynb</em> to demonstrate how to access the data.</p> <p>Each file is structured as (except the files under *"training/"* folder):<br> </p> <pre><code>|- csi_imag |- csi_real |- nPaths_1 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_2 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_3 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_4 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- num_obj |- obj_0 |- cam_aoa |- coordinates |- obj_1 |- cam_aoa |- coordinates ... |- timestamp </code></pre> <p>The `csi_real` and `csi_imag` are the real and imagenary part of the CSI measurements. <strong>The order of antennas and subcarriers are as follows for the 90 `csi_real` and `csi_imag` values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]</strong>. `nPaths_x` group are SpotFi [2] calculated WiFi Angle of Arrival (AoA) with `x` number of multiple paths specified during calculation. Under the `nPath_x` group are `offset_xx` subgroup where `xx` stands for the offset combination used to correct the phase offset during the SpotFi calculation. We measured the offsets as:</p> <pre><code>|Antennas | Offset 1 (rad) | Offset 2 (rad) | |:-------:|:---------------:|:-------------:| | 1 & 2 | 1.1899 | -2.0071 | 1 & 3 | 1.3883 | -1.8129 </code></pre> <p>The measurement is based on the work [3], where the authors state there are two possible offsets between two antennas which we measured by booting the device multiple times. The combination of the offset are used for the `offset_xx` naming. For example, `offset_12` is offset 1 between antenna 1 & 2 and offset 2 between antenna 1 & 3 are used in the SpotFi calculation.</p> <p>The `num_obj` field is used to store the number of human subjects present in the scene. The `obj_0` is always the subject who is holding the phone. In each file, there are `num_obj` of `obj_x`. For each `obj_x1`, we have the `coordinates` reported from the camera and `cam_aoa`, which is estimated AoA from the camera reported coordinates. The (x,y) coordinates and AoA listed here are chronologically ordered (except the files in the `training` folder) . It reflects the way the person carried the phone moved in the space (for `obj_0`) and everyone else walked (for other `obj_y`, where `y` > 0). </p> <p>The `timestamp` is provided here for time reference for each WiFi packets.</p> <p>To access the data (Python):</p> <pre><code class="language-python">import h5py data = h5py.File('3_people_3.h5','r') csi_real = data['csi_real'][()] csi_imag = data['csi_imag'][()] cam_aoa = data['obj_0/cam_aoa'][()] cam_loc = data['obj_0/coordinates'][()] </code></pre> <p><strong>For file inside `training/` folder:</strong></p> <p>Files inside training folder has a different data structure:</p> <pre><code> |- nPath-1 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-2 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-3 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-4 |- aoa |- csi_imag |- csi_real |- spotfi </code></pre> <p><br> The group `nPath-x` is the number of multiple path specified during the SpotFi calculation. `aoa` is the camera generated angle of arrival (AoA) (can be considered as ground truth), `csi_image` and `csi_real` is the imaginary and real component of the CSI value. `spotfi` is the SpotFi calculated AoA values. The SpotFi values are chosen based on the lowest median and mean error from across `1_person_1.h5` and `1_person_2.h5`. All the rows under the same `nPath-x` group are aligned (i.e., first row of `aoa` corresponds to the first row of `csi_imag`, `csi_real`, and `spotfi`. There is no timestamp recorded and the sequence of the data is not chronological as they are randomly shuffled from the `1_person_1.h5` and `1_person_2.h5` files. </p> <p><strong>Citation</strong><br> If you use the dataset, please cite our paper:</p> <pre><code>@inproceedings{eyefi2020, title={EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching}, author={Fang, Shiwei and Islam, Tamzeed and Munir, Sirajum and Nirjon, Shahriar}, booktitle={2020 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)}, year={2020}, organization={IEEE} } </code></pre> <p>Thanks!</p> <p><strong>References</strong></p> <p>1. Halperin, Daniel, et al. "Tool release: Gathering 802.11 n traces with channel state information." ACM SIGCOMM Computer Communication Review 41.1 (2011): 53-53.</p> <p>2. Kotaru, Manikanta, et al. "Spotfi: Decimeter level localization using wifi." Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. 2015.</p> <p>3. Zhang, Dongheng, et al. "Calibrating Phase Offsets for Commodity WiFi." IEEE Systems Journal (2019).<br> </p>
WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32
<p><strong>WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32</strong></p> <p>This repository contains the WiFi CSI human presence detection and activity recognition datasets proposed in [1].</p> <p><strong>Datasets</strong></p> <ul> <li><strong>DP_LOS</strong> - Line-of-sight (LOS) presence detection dataset, comprised of 392 CSI amplitude spectrograms.</li> <li><strong>DP_NLOS </strong>- Non-line-of-sight (NLOS) presence detection dataset, comprised of 384 CSI amplitude spectrograms.</li> <li><strong>DA_LOS</strong> - LOS activity recognition dataset, comprised of 392 CSI amplitude spectrograms.</li> <li><strong>DA_NLOS</strong> - NLOS activity recognition dataset, comprised of 384 CSI amplitude spectrograms.</li> </ul> <p>Table 1: Characteristics of presence detection and activity recognition datasets. </p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>Scenario</strong></td> <td><strong>#Rooms</strong></td> <td><strong>#Persons</strong></td> <td><strong>#Classes</strong></td> <td><strong>Packet Sending Rate</strong></td> <td><strong>Interval </strong></td> <td><strong>#Spectrograms</strong></td> </tr> <tr> <td>DP_LOS</td> <td>LOS</td> <td>1</td> <td>1</td> <td>6</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>392</td> </tr> <tr> <td>DP_NLOS</td> <td>NLOS</td> <td>5</td> <td>1</td> <td>6</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>384</td> </tr> <tr> <td>DA_LOS</td> <td>LOS</td> <td>1</td> <td>1</td> <td>3</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>392</td> </tr> <tr> <td>DA_NLOS</td> <td>NLOS</td> <td>5</td> <td>1</td> <td>3</td> <td>100Hz</td> <td>4s (400 packets)</td> <td>384</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Format</strong></p> <p>Each dataset employs an 8:1:1 training-validation-test split, defined in the provided label files <em>trainLabels.csv</em>, <em>validationLabels.csv</em>, and <em>testLabels.csv</em>. Label files use the sample format [<em>i c</em>], with <em>i</em> corresponding to the spectrogram index (i.png) and <em>c </em>corresponding to the class. For presence detection datasets (DP_LOS <em>, </em>DP_NLOS), c in {0 = "no presence", 1 = "presence in room 1", ..., 5 = "presence in room 5"}. For activity recognition datasets (DA_LOS <em>, </em>DA_NLOS), c in {0="no activity", 1="walking", and 2="walking + arm-waving"}. Furthermore, the mean and standard deviation of a given dataset are provided in <em>meanStd.csv</em>.</p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] Strohmayer, Julian, and Martin Kampel. "WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32" <em>International Conference on Computer Vision Systems</em>. Cham: Springer Nature Switzerland, 2023. </p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayer2023wifi, title={WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={International Conference on Computer Vision Systems}, pages={41--50}, year={2023}, organization={Springer} }</pre>
WACA WiFi 5-GHz dataset
<p>This folder contains the dataset presented in "<em>Sergio Barrachina-Muñoz, Boris Bellalta, and Edward Knightly. 2020. Wi-Fi All-Channel Analyzer. WinTech (2020)</em>".</p> <p>Please, refer to the GitHub repository for more details:<br> <a href="https://github.com/sergiobarra/WACA_WiFiAnalyzer">https://github.com/sergiobarra/WACA_WiFiAnalyzer</a></p> <p>-------------------------------------------------------------<br> <strong>*** Dataset structure ***</strong><br> The dataset is composed of 11 scenarios, each with a corresponding folder (campaign duration within brackets):</p> <ul> <li>1_RVA: Rice Village Apartments, Houston; Apartment; 15th February 2019; 9600 iterations (~1 day)</li> <li>2_RNG: Rice Networks Group, Houston; Campus office; 19th February 2019; 8750 iterations (~1 day)</li> <li>3_TFA: Technology for All, Houston; Community center; 20th February 2019; 8609 iterations (~1 day)</li> <li>4_FLO: Flo Paris (at Rice Village), Houston; Cafe; 23rd February 2019; 413 iterations (~1 hour)</li> <li>5_VIL: Rice Village parking lot, Houston; Shopping mall; 23rd February 2019; 125 iterations (~20 min)</li> <li>6_FEL: La Sagrera neighborhood, Barcelona; Apartment; 25th March 2019; 59500 iterations (~1 week)</li> <li>7_WNO: Wireless Networking, Barcelona; Campus office; 3rd April 2019; 9600 iterations (~1 day)</li> <li>8_22A: 22@ neighborhood, Barcelona; Office area; 2nd July 2019; 9600 iterations (~1 day)</li> <li>9_GAL: Hotel Gallery, Barcelona; Downtown hotel; 10th July 2019; 9706 iterations (~1 day)</li> <li>10_SAG: Sagrada Familia, Barcelona; Apartment; 11th February 2019; 38192 iterations (~4 days)</li> <li>11_FCB: Camp Nou stadium, Barcelona; Futbol (soccer) Stadium; 4th August; 2001 iterations (~ 5 hours)</li> </ul> <p><strong>*** File format ***</strong></p> <ul> <li>RSSI measurements are stored in .mat files inside each scenario folder.</li> <li>.mat files are named "it<iteration number>_<mm-dd-yy>_<hh:mm:ss>.mat". For instance file "it0251_02-19-19_08-03-09.mat" refers to iteration number 251, initiated on 19th February 2019 at 08:03:09.</li> <li>Every .mat file contains 24 arrays 10000x1, one per basic channel, where the <em>i</em>th element represents the RSSI value at sample <em>i </em>(of duration 10 microseconds) inside the iteration (of duration 1 second).</li> <li>The RSSI value is given in the units of the MAX2829 transceiver (10-bit values). Please, refer to the transceiver datasheet or to our GitHub repository to see how to convert from 10-bit values to dBm.</li> <li>Finally, every scenario folder also contains the "experiment_general.mat". This file is auxiliary and contains different constants of interest related to the configuration of the campaigns.</li> </ul> <p><strong>*** How do I process the dataset? ***</strong><br> We provide the RSSI measurements for every channel in every scenario. You may generate a Matlab snippet to process the data.<br> Nevertheless, the Matlab code we used in the paper "<em>Sergio Barrachina-Muñoz, Boris Bellalta, and Edward Knightly. 2020. Wi-Fi All-Channel Analyzer. WinTech (2020)</em>" can be found in the GitHub repository.</p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (tech. report 2013)
<p>This dataset contains the data used in:</p> <p>Danalet, A., Farooq, B. and Bierlaire, M. (2013). A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures, Technical report, Transport and Mobility Laboratory, ENAC, Ecole Polytechnique Fédérale de Lausanne, Lausanne. URL: http://infoscience.epfl.ch/record/189759 (full text available)</p> <p>It contains data and a technical report describing</p> <ul> <li>WiFi traces</li> <li>Pedestrian Semantically-Enriched Routing Graph (SERG), and</li> <li>Potential Attractivity measure (PAM).</li> </ul>
WiFi RTT RSS dataset for indoor positioning
<p>This is the first batch of WiFi RSS RTT datasets with LOS conditions we published. Please see <code>https://doi.org/10.5281/zenodo.11558792</code> for the second batch.</p> <h2><strong>Please do use version 2 for better quality.</strong></h2> <p>We provide publicly available datasets of three different indoor scenarios: building floor, office and apartment. The datasets contain both <strong>WiFi RSS and RTT signal measures</strong> with <strong>groud truth coordinates</strong> label and <strong>LOS condition</strong> label.</p> <p>1.Building Floor</p> <p>This is a detailed WiFi RTT and RSS dataset of a whole floor of a university building, of moare than 92 x 15 square metres. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures recorded in 642 reference points for 3 days and is well separated so that training points and testing points will not overlap.</p> <p>2. Office</p> <p>Office scenario is of more than 4.5 x 5.5 square metres. 3 APs are set to cover the whole space. At least two LOS AP could be seen at any reference point (RP). </p> <p>3.Apartment</p> <p>Apartment scenario is of more than 7.7 x 9.4 square metres.Four APs were leveraged to generate WiFi signal measures for this testbed. Note that AP 1 in the apartment dataset was positioned so that it could had an NLOS path to most of the testbed. </p> <p> </p> <h2><strong>Collection methodology</strong></h2> <p>The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.</p> <p>The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers. </p> <p>Only RTT-enabled APs were included in the dataset.</p> <h2><strong>The features of the datasets</strong></h2> <p><strong>The features of the building floor dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 92 × 15 m2</p> <p>Grid size: 0.6 × 0.6 m2</p> <p>Number of AP: 13</p> <p>Number of reference points: 642</p> <p>Samples per reference point: 120</p> <p>Number of all data samples: 77040</p> <p>Number of training samples: 57960</p> <p>Number of testing samples: 19080</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Collection time interval: 3 days</p> </blockquote> <p><strong>The features of the office dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 4.5 × 5.5 m2</p> <p>Grid size: 0.455 × 0.455 m2</p> <p>Number of AP: 3</p> <p>Reference points: 37</p> <p>Samples per reference point: 120</p> <p>Data samples: 4,440</p> <p>Training samples: 3,240</p> <p>Testing samples: 1,200</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Other information: LOS condition of every AP</p> <p>Collection time: 1 day</p> <p>Notes: A LOS scenario</p> </blockquote> <p><strong>The features of the apartment dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 7.7 × 9.4 m2</p> <p>Grid size: 0.48 × 0.48 m2</p> <p>Number of AP: 4</p> <p>Reference points: 110</p> <p>Samples per reference point: 120</p> <p>Data samples: 13,200</p> <p>Training samples: 9,720</p> <p>Testing samples: 3,480</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Other information: LOS condition of every AP</p> <p>Collection time: 1 day</p> <p>Notes: Contains an AP with NLOS paths for most of the RPs</p> </blockquote> <h2><strong>Dataset explanation</strong></h2> <p>The columns of the dataset are as follows:</p> <p>Column 'X': the X coordinates of the sample.</p> <p>Column 'Y': the Y coordinates of the sample.</p> <p>Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP13 RTT(mm)': the RTT measure from corresponding AP at a reference point.</p> <p>Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP13 RSS(dBm)': the RSS measure from corresponding AP at a reference point.</p> <p>Column 'LOS APs': indicating which AP has a LOS to this reference point.</p> <p>Please note:</p> <ul> <li>The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.</li> <li>The RTT value 100,000 mm indicates that no signal is received from the specific AP.</li> </ul> <h2><strong>Citation request<br></strong></h2> <p>When using this dataset, please cite the following two items:<br><br><code>Feng, X., Nguyen, K. A., & Luo, Z. (2024). WiFi RTT RSS dataset for indoor positioning [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11558192" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11558192</a></code><br><br><code>@article{feng2023wifi, title={WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments}, author={Feng, Xu and Nguyen, Khuong an and Luo, Zhiyuan}, journal={Journal of Location Based Services}, volume={17}, number={4}, pages={307--339}, year={2023}, publisher={Taylor \& Francis} }</code></p>
Crowdsourced WiFi database and benchmark software for indoor positioning
<p>This dataset contains two Wi-Fi databases (one for training and one for test/estimation purposes in indoor positioning applications), collected in a crowdsourced mode (i.e., via 21 different devices and different users), together with a benchmarking utility software (in Matlab and Python) to illustrate various algorithms of indoor positioning based solely on WiFi information (MAC addresses and RSS values). </p> <p>The data was collected in a 4-floor university building in Tampere, Finland, during Jan-Aug 2017 and it comprises 687 training fingerprints and 3951 test or estimation fingerprints.</p> <p>13.10.2017: Version 2 uploaded; the revised version contains improved readme files and improved Python SW.</p> <p>The dataset and/or the associated software are to be cited as follows:</p> <p>E.S. Lohan, J. Torres-Sospedra, P. Richter, H. Leppäkoski, J. Huerta, A. Cramariuc, “Crowdsourced WiFi-fingerprinting database and benchmark software for indoor positioning”, Zenodo repository, DOI 10.5281/zenodo.889798</p>
Location choice with longitudinal WiFi data: Research Data
<p>This document describes the softwares, data, models and raw results associated to</p> <blockquote> <p>Danalet, A., Tinguely, L., Lapparent, M. d. and Bierlaire, M. (2016). Location choice with longitudinal WiFi data, Journal of Choice Modelling 18: 1–17. URL: https://dx.doi.org/10.1016/j.jocm.2016.04.003</p> </blockquote>
ScienceDex guides
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